scholarly journals Deep Learning, a Not so Magical Problem Solver: A Case Study with Predicting the Complexity of Breast Cancer Cases

2021 ◽  
Author(s):  
My-Anh Le Thien ◽  
Akram Redjdal ◽  
Jacques Bouaud ◽  
Brigitte Seroussi
Keyword(s):  
Breast Cancer ◽  
Decision Support ◽  
Patient Management ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Machine Learning Algorithms ◽  
Problem Solver ◽  
Multi Layer Perceptron ◽  
Complex Cases

Using guideline-based clinical decision support systems (CDSSs) has improved clinical practice, especially during multidisciplinary tumour boards (MTBs) in cancer patient management. However, MTBs have been reported to be overcrowded, with limited time to discuss all cases. Complex breast cancer cases that need further MTB discussions should have priority in the organization of MTBs. In order to optimize MTB workflow, we attempted to predict complex cases defined as non-compliant cases despite the use of the decision support system OncoDoc. After previously obtaining insufficient performance with machine learning algorithms, we tested Multi Layer Perceptron for classification, compared various samplers to compensate data imbalance combined with cross- validation, and optimized all models with hyperparameter tuning and feature selection with no improvement and lacklustre results (F1-score: 31.4%).

scholarly journals Are Semantic Annotators Able to Extract Relevant Complexity-Related Concepts from Clinical Notes?

2021 ◽  
Author(s):  
Akram Redjdal ◽  
Jacques Bouaud ◽  
Joseph Gligorov ◽  
Brigitte Séroussi
Keyword(s):  
Breast Cancer ◽  
Clinical Practice ◽  
Decision Support ◽  
Cancer Patient ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Clinical Notes ◽  
Tumor Boards ◽  
Performance Rate ◽  
Complex Cases

Clinical decision support systems (CDSSs) implementing cancer clinical practice guidelines (CPGs) have the potential to improve the compliance of decisions made by multidisciplinary tumor boards (MTB) with CPGs. However, guideline-based CDSSs do not cover complex cases and need time for discussion. We propose to learn how to predict complex cancer cases prior to MTBs from breast cancer patient summaries (BCPSs) resuming clinical notes. BCPSs being unstructured natural language textual documents, we implemented four semantic annotators (ECMT, SIFR, cTAKES, and MetaMap) to assess whether complexity-related concepts could be extracted from clinical notes. On a sample of 24 BCPSs covering 35 complexity reasons, ECMT and MetaMap were the most efficient systems with a performance rate of 60% (21/35) and 49% (17/35), respectively. When using the four annotators in sequence, 69% of complexity reasons were extracted (24/35 reasons).

scholarly journals Clinical Decision Support Systems in Breast Cancer: A Systematic Review

Cancers ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 369 ◽  
Author(s):  
Claudia Mazo ◽  
Cathriona Kearns ◽  
Catherine Mooney ◽  
William M. Gallagher
Keyword(s):  
Breast Cancer ◽  
Systematic Review ◽  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Major Advance

Breast cancer is the most frequently diagnosed cancer in women, with more than 2.1 million new diagnoses worldwide every year. Personalised treatment is critical to optimising outcomes for patients with breast cancer. A major advance in medical practice is the incorporation of Clinical Decision Support Systems (CDSSs) to assist and support healthcare staff in clinical decision-making, thus improving the quality of decisions and overall patient care whilst minimising costs. The usage and availability of CDSSs in breast cancer care in healthcare settings is increasing. However, there may be differences in how particular CDSSs are developed, the information they include, the decisions they recommend, and how they are used in practice. This systematic review examines various CDSSs to determine their availability, intended use, medical characteristics, and expected outputs concerning breast cancer therapeutic decisions, an area that is known to have varying degrees of subjectivity in clinical practice. Utilising the methodology of Kitchenham and Charter, a systematic search of the literature was performed in Springer, Science Direct, Google Scholar, PubMed, ACM, IEEE, and Scopus. An overview of CDSS which supports decision-making in breast cancer treatment is provided along with a critical appraisal of their benefits, limitations, and opportunities for improvement.

scholarly journals Development of a Scalable Tele-Monitoring Platform based on the Clinical Decision Support Systems

2020 ◽  
Author(s):  
azita yazdani ◽  
Reza Safdari ◽  
Roxana Sharifian ◽  
maryam zahmatkeshan
Keyword(s):  
Health Care ◽  
Performance Evaluation ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Monitoring Systems ◽  
Tele Monitoring

Abstract Background: One of the most important types of information systems that play important role today in providing quality health care services are clinical decision support systems (CDSSs). These systems are effective in overcoming human resource constraint and intelligent analysis of information generated by Tele-monitoring systems. In spite of the many advantages of this architectures, these are single-purpose, meaning that only the CDSS of a disease is located on them. If we want to use the same model of architecture in the decision-making process of another disease, all the components of this architecture should be redevelopment with a new CDSS, which is time-consuming and costly. Due to the increasing demand for health information technology at low cost and mobile access in the health care industry, in this article, a scalable software platform(Patient Tele monitoring: PATEL) based on SOA for implementing and use different CDSSs on a common platform, for use in Tele-monitoring Systems, was created.Implementation: To develop PATEL platform, the component-based software development approach and hybrid programming approach to implementing various components used. In the evaluation phase of the proposed platform, the case study, accuracy and performance evaluation (transmission delays, patient data fetch, parsing overhead and inference time) used.Results: The results of the case study evaluation confirmed the scalability and interoperability between CDSSs on the platform. Based on performance evaluation, the proposed platform has responded to 89% of the requests in less than one second. Also, based on accuracy evaluation, the platform presented in this article was successful in diagnosing 91.6% of the cases.Conclusion: The proposed platform can support CDSSs of various diseases simultaneously and provides the necessary scalability to add a new CDSS. Tele-monitoring systems will be capable of service by connecting to this platform. Using this infrastructure is expected to be a lot of duplication in the implementation of tele-monitoring systems based CDSSs will be reduced.

scholarly journals Assessing the Safety of Custom Web-Based Clinical Decision Support Systems in Electronic Health Records: A Case Study

2019 ◽  
Vol 10 (02) ◽  
pp. 237-246 ◽  
Author(s):  
Jeritt Thayer ◽  
Jeffrey Miller ◽  
Alexander Fiks ◽  
Linda Tague ◽  
Robert Grundmeier
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Care ◽  
Clinical Decision Support Systems ◽  
High Volume ◽  
Clinical Decision ◽  
Clinical Workflow ◽  
System Integrity ◽  
Electronic Health

Background With the widespread adoption of vendor-supplied electronic health record (EHR) systems, clinical decision support (CDS) customization efforts beyond those anticipated by the vendor may require the use of technologies external to the EHR such as web services. Pursuing such customizations, however, is not without risk. Validating the expected behavior of a customized CDS system in the high-volume, complex environment of the live EHR is a challenging problem. Objective This article identifies technology failures that impacted clinical care related to web service-based advanced custom CDS systems embedded in the complex sociotechnical context of a production EHR. Methods In an academic health system’s primary care network, we performed an inventory of incidents between January 1, 2008 and December 31, 2016 related to a customized CDS system and performed a targeted review of changes in the CDS source code. Additional feedback on the root cause of individual incidents was obtained through interviews with members of the CDS project teams. Results We identified five CDS malfunctions that impaired clinical workflow. The mechanisms for these failures are mapped to four characteristics of well-behaved applications: (1) system integrity; (2) data integrity; (3) reliability; and (4) scalability. Over the 9-year period, two malfunctions of the customized CDS significantly impaired clinical workflow for a total of 5 hours. Lesser impacts—loss of individual features with straightforward workarounds—arose from three malfunctions, which affected users on 53 days. Discussion Advanced customization of EHRs for the purpose of CDS can present significant risks to clinical workflow. Conclusion This case study highlights that advanced customization of CDS within a commercial EHR may support care for complex patient populations, but ongoing monitoring and support is required to ensure its safe use.

scholarly journals Reusing Decisions Made with One Decision Support System to Assess a Second Decision Support System: Introducing the Notion of Complex Cases

2021 ◽  
Author(s):  
Akram Redjdal ◽  
Jacques Bouaud ◽  
Gilles Guézennec ◽  
Joseph Gligorov ◽  
Brigitte Seroussi
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Gold Standard ◽  
Compliance Rate ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Tumor Board ◽  
Complex Cases ◽  
Clinical Cases

The guideline-based decision support system (GL-DSS) of the DESIREE project and OncoDoc are two clinical decision support systems applied to the management of breast cancer. In order to evaluate the DESIREE GL-DSS, we decided to reuse a sample of clinical cases previously resolved by the multidisciplinary tumor board (MTB) of the Tenon Hospital (Paris, France) when using OncoDoc. Since we had two different knowledge representation models to represent clinical parameters and decisions, and two formalisms to represent guidelines, we developed a transformation sequence, involving the creation of synthetic patients, the enrichment of DESIREE ontology, and the translation of clinical cases and their decisions, to transform OncoDoc data into the DESIREE representation. Considering MTB decisions as the gold standard, the 84% compliance rate of DESIREE recommendations was rather satisfactory. Some situations (0.7%) concerned clinical cases that were compliant neither with OncoDoc nor with DESIREE that we defined as complex cases, not handled by guidelines, which necessitate effective MTB discussions.

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